A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies
In recent years, governments of more than 200 countries and regions have enacted measures to control the spread of COVID-19. A precise and comprehensive evaluation of policy effect provides important grounds for policy-making. Since the whole world has entered the post-epidemic era, prevention polic...
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Tsinghua University Press
2024-12-01
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| Series: | CAAI Artificial Intelligence Research |
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| Online Access: | https://www.sciopen.com/article/10.26599/AIR.2024.9150034 |
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| author | Zhoujingming Gao Zhiyi Tan Bing-Kun Bao |
| author_facet | Zhoujingming Gao Zhiyi Tan Bing-Kun Bao |
| author_sort | Zhoujingming Gao |
| collection | DOAJ |
| description | In recent years, governments of more than 200 countries and regions have enacted measures to control the spread of COVID-19. A precise and comprehensive evaluation of policy effect provides important grounds for policy-making. Since the whole world has entered the post-epidemic era, prevention policies are inclined to strike a trade-off between controlling confirmed/death cases and the economic rebound. Furthermore, with the increasing vaccination rate, vaccination has become a considerable factor in determining policy stringency. However, the existing approaches are still limited in efficiency due to the following reasons: (1) They are still confined to policies’ containment effect on COVID-19, neglecting the impact of vaccination on policy effect and the impact of policies on economy; (2) While evaluating policy effect in different regions, most existing models lack robustness. To address these problems, we propose a multi-dimensional evaluation model for more effective assessment of epidemic prevention policies in post-epidemic era. The proposed model consists of two modules: (1) A multi-dimensional objective-programming module is raised to evaluate the policy effect comprehensively, where vaccination, policy stringency, economy indicators, confirmed cases, and reproductive rate are taken into account; (2) A vaccine-dependent parameter learning (VDPL) module based on Bayesian deep learning (BDL) models a vaccine-dependent parameter which indicates the relationship between vaccination and policy stringency. The module also strengthens the robustness of the proposed model with the help of BDL since BDL can adapt the data of different regions better through resampling the probability distribution of network weights. Finally, We evaluate our model on the data of the US. The results demonstrate that the proposed approach performs better in depicting the spread of COVID-19 under the influence of policy. |
| format | Article |
| id | doaj-art-64d4e119663a4567b2f64214fb94bf00 |
| institution | Kabale University |
| issn | 2097-194X 2097-3691 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | CAAI Artificial Intelligence Research |
| spelling | doaj-art-64d4e119663a4567b2f64214fb94bf002025-01-10T06:44:32ZengTsinghua University PressCAAI Artificial Intelligence Research2097-194X2097-36912024-12-013915003410.26599/AIR.2024.9150034A Multi-Dimensional Evaluation Model for Epidemic Prevention PoliciesZhoujingming Gao0Zhiyi Tan1Bing-Kun Bao2School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210000, ChinaSchool of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210000, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210000, ChinaIn recent years, governments of more than 200 countries and regions have enacted measures to control the spread of COVID-19. A precise and comprehensive evaluation of policy effect provides important grounds for policy-making. Since the whole world has entered the post-epidemic era, prevention policies are inclined to strike a trade-off between controlling confirmed/death cases and the economic rebound. Furthermore, with the increasing vaccination rate, vaccination has become a considerable factor in determining policy stringency. However, the existing approaches are still limited in efficiency due to the following reasons: (1) They are still confined to policies’ containment effect on COVID-19, neglecting the impact of vaccination on policy effect and the impact of policies on economy; (2) While evaluating policy effect in different regions, most existing models lack robustness. To address these problems, we propose a multi-dimensional evaluation model for more effective assessment of epidemic prevention policies in post-epidemic era. The proposed model consists of two modules: (1) A multi-dimensional objective-programming module is raised to evaluate the policy effect comprehensively, where vaccination, policy stringency, economy indicators, confirmed cases, and reproductive rate are taken into account; (2) A vaccine-dependent parameter learning (VDPL) module based on Bayesian deep learning (BDL) models a vaccine-dependent parameter which indicates the relationship between vaccination and policy stringency. The module also strengthens the robustness of the proposed model with the help of BDL since BDL can adapt the data of different regions better through resampling the probability distribution of network weights. Finally, We evaluate our model on the data of the US. The results demonstrate that the proposed approach performs better in depicting the spread of COVID-19 under the influence of policy.https://www.sciopen.com/article/10.26599/AIR.2024.9150034covid-19bayesian deep learning (bdl)vaccineoptimal policies |
| spellingShingle | Zhoujingming Gao Zhiyi Tan Bing-Kun Bao A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies CAAI Artificial Intelligence Research covid-19 bayesian deep learning (bdl) vaccine optimal policies |
| title | A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies |
| title_full | A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies |
| title_fullStr | A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies |
| title_full_unstemmed | A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies |
| title_short | A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies |
| title_sort | multi dimensional evaluation model for epidemic prevention policies |
| topic | covid-19 bayesian deep learning (bdl) vaccine optimal policies |
| url | https://www.sciopen.com/article/10.26599/AIR.2024.9150034 |
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